<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1815-5928</journal-id>
<journal-title><![CDATA[Ingeniería Electrónica, Automática y Comunicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[EAC]]></abbrev-journal-title>
<issn>1815-5928</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59282019000100018</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Clasificador automático de imágenes de muestras de sangre basado en redes neuronales profundas]]></article-title>
<article-title xml:lang="en"><![CDATA[Automatic Classifier of blood sample images based on deep neural networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pla Martínez]]></surname>
<given-names><![CDATA[Gilbert]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Irizar Mesa]]></surname>
<given-names><![CDATA[Mirtha]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnológica de La Habana José Antonio Echeverría  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2019</year>
</pub-date>
<volume>40</volume>
<numero>1</numero>
<fpage>18</fpage>
<lpage>30</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59282019000100018&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59282019000100018&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59282019000100018&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El tamizaje neonatal masivo es una prueba que se realiza a todos los recién nacidos para detectar y prevenir enfermedades congénitas, hereditarias y metabólicas que afectan el crecimiento y desarrollo normal del niño. Esta prueba consiste en realizar una pequeña punción en el talón del bebé para tomar unas pocas gotas de sangre y colocarlas en un papel filtro para ser analizadas en el laboratorio. Una de las etapas de este análisis es la evaluación de la calidad de las muestras basándose en elementos como la coloración de las muestras, el secado, la presencia de impurezas y la presencia de coágulos. Debido a que esta etapa requiere de elevada experiencia, agilidad y memoria visual de los especialistas, en ocasiones se llevan muestras al laboratorio que no brindan los mejores resultados por la baja calidad que presentan. Este trabajo presenta un estudio para desarrollar un clasificador automático basado en redes neuronales profundas que evalúe la calidad de las muestras de sangre analizadas. Este clasificador se basa en el reconocimiento de patrones asociados a los momentos de color y a las componentes de matiz, saturación y valor del brillo, extraídos de las imágenes de las muestras de sangre. Para esto se seleccionó una red neuronal profunda compuesta por dos autocodificadores profundos más un clasificador softmax, entrenada con ejemplos de imágenes de muestras de sangre, obteniéndose resultados satisfactorios en la validación del método con la clasificación correcta de las muestras sometidas a análisis.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Massive neonatal screening is a test performed on all newborns to detect and prevent congenital, hereditary and metabolic diseases that affect the child's normal growth and development. This test consists of making a small puncture in the heel of the baby to take a few drops of blood and place them on a filter paper to be analyzed in the laboratory. One of the stages of this analysis is the quality evaluation of the samples based on elements such as the coloring of the samples, the drying, the presence of impurities and the presence of clots. Because this stage requires high experience, agility and visual memory of the specialists, sometimes samples are taken to the laboratory and they do not provide the best results due to the low quality they present. This paper presents a study to develop an automatic classifier based on deep artificial neural networks that evaluates the quality of the blood samples analyzed. This classifier is based on pattern recognition associated to the color moments and the HSV components, extracted from the images of blood samples. For this, a deep neural network formed by two deep autoencoders and a softmax classifier is trained with examples of images of blood samples, obtaining satisfactory results in the validation of the method with the correct classification of the samples submitted for analysis]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[tamizaje neonatal]]></kwd>
<kwd lng="es"><![CDATA[momentos de color]]></kwd>
<kwd lng="es"><![CDATA[componentes HSV]]></kwd>
<kwd lng="es"><![CDATA[autocodificadores profundos]]></kwd>
<kwd lng="en"><![CDATA[neonatal screening]]></kwd>
<kwd lng="en"><![CDATA[color moments]]></kwd>
<kwd lng="en"><![CDATA[HSV components]]></kwd>
<kwd lng="en"><![CDATA[deep autoencoders]]></kwd>
</kwd-group>
</article-meta>
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